Method for learning multivalued mapping

ABSTRACT

A method for learning multivalued mapping comprises the steps of: mathematically expressing a multivalued function directly in Kronecker&#39;s tensor product form; developing and replacing the tensor product form so as to obtain a linear equation with respect to unknown functions; defining a sum of a linear combination of local base functions and a linear combination of polynomial bases with respect to the replaced unknown function; and learning or structuring a manifold which is defined by the linearized functions in the input-output space, from example data, through use of a procedure for optimizing the error and the smoothness constraint. Therefore, mapping learning can be performed from a small amount of data.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for structuring a computing module for learning non-linear mapping which is generally a multivalued mapping. More specifically, the invention relates to a method for learning multivalued mapping by a functional approximation which enables learning to obtain an overview of the multifold structure of the mapping from a small amount of example data and also enables learning local changes.

2. Description of the Related Art

The inventor of the present invention proposed a method for multivalued function approximation (see Japanese Patent Application Laid Open (kokai) No. 7-93296, “Method for Learning Multivalued Function” and Transactions of the Institute of Electronics, Information and Communication Engineers of Japan A, vol. J78-A, No. 3, pp. 427-439 (1995) “Multivalued Regularization Network”).

However, in the above-mentioned method for multivalued function approximation, only locally defined base functions are used, although multivalued functions can be expressed. Namely, F_(k) is expressed by a function of a linear sum of the local base functions centering on {right arrow over (t)}_(kp), as follows: $\begin{matrix} {{F_{k}\left( \overset{\rightarrow}{x} \right)} = {\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{t}}_{kp}} \right)}}}} & (1) \end{matrix}$

The above mentioned Japanese Patent Application Laid Open No. 7-93296 describes an example wherein the center {right arrow over (t)}_(kp) coincides with the input portion {right arrow over (x)}_((i)) of the teaching data.

However, in the above-mentioned method for multivalued function approximation, since base functions are defined only in the vicinity of the teaching data or the center {right arrow over (t)}_(kp), functions are not defined where the teaching data do not exist. Another problem is that when the numbers of input and output space dimensions (m+n) become large the required amount of teaching data remarkably increases.

SUMMARY OF THE INVENTION

An object of the present invention is to solve the above-mentioned problems and to provide a method for learning multivalued mapping.

The method for learning multivalued mapping according to the present invention provides a method for approximation of a manifold in (n+m)-dimensional space expressing a mapping computation module having n inputs and m outputs.

According to a first aspect of the present invention, there is provided a method for learning multivalued mapping for providing a method for approximation of manifold in (n+m)-dimensional space, by learning a smooth function from n-dimensional input space to m-dimensional output space which optimally approximates m-dimensional vector value data forming a given plurality of layers in n-dimensional space, the method comprising steps of:

(a) mathematically expressing a multivalued function directly in Kronecker's tensor product form;

(b) developing and replacing the functions so as to obtain linear equations with respect to unknown functions;

(c) defining the sum of a linear combination of local base functions and a linear combination of polynomial bases with respect to the replaced unknown function; and

(d) learning from example data, an manifold which is defined by the linearized function in the input-output space, through use of a procedure for optimizing the error and the smoothness constraint.

According to a second aspect of the present invention, there is provided a method for learning multivalued mapping for providing a method for approximation of manifold in (n+m)-dimensional space, by obtaining a smooth function from n-dimensional input space to m-dimensional output space which optimally approximates m-dimensional vector value data forming a given plurality of layers in n-dimensional space, the method comprising steps of:

(a) expressing a manifold by synthesizing h m-dimensional vector value functions in n-dimensional space according to the following equation:

{{right arrow over (y)}−f ₁({right arrow over (x)})}{{right arrow over (y)}−f ₂({right arrow over (x)})} . . . {{right arrow over (y)}−f _(h)({right arrow over (x)})}=0  (2)

wherein “” denotes Kronecker's tensor product,

{right arrow over (x)}=(x ₁ ,x ₂ , . . . x _(n))^(T),

{right arrow over (y)}=(y ₁ ,y ₂ , . . . y _(m))^(T),

and “T” denotes transposition of a vector;

(b) developing the above equation and converting it into a linear equation with respect to unknown functions;

(c) expressing each unknown function as: $\begin{matrix} {{{{F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{t}}_{kp}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}}}}};}\quad} & (3) \end{matrix}$

(d) defining an error functional for calculating the error on the left side of Equation (2) for calculating the unknown function F_(k) from the example data;

(e) defining a regularizing functional as the square of the absolute value of the result of the operation in which the operator defining the smoothness constraint of the unknown function is applied to each unknown function, as required,

(f) minimizing the error functional and the regularizing functional, and deriving a procedure for obtaining the unknown functions F_(k); and

(g) obtaining a conversion function for calculating f_(j) from the unknown functions F_(k) by formula manipulation method or by numerical approximation algorithm.

In the method for learning multivalued mapping according to the second aspect, m may be 1 or 2.

In the method for learning multivalued mapping according to the second aspect, the equality {right arrow over (t)}_(0p)={right arrow over (t)}_(1p)={right arrow over (t)}_(2p)= . . . ={right arrow over (t)}_(hp) may be satisfied.

In the method for learning multivalued mapping according to the second aspect, N pairs of example data may be [({right arrow over (x)}_((i)), {right arrow over (y)}_((i)))|i=1, 2, . . . , N], and M=N, {right arrow over (t)}_(kp)={right arrow over (x)}_((p)) (wherein p=1, 2, . . . , N).

In the method for learning multivalued mapping according to the second aspect, the equality K_(k1)=K_(k2)= . . . =K_(kM) (wherein k=0, 1, 2, . . . , h) may be satisfied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing the method for learning multivalued mapping according to the present invention;

FIG. 2 is a block diagram showing a mapping calculation module for the method for learning multivalued mapping according to the present invention; and

FIG. 3 is a detailed block diagram showing a mapping calculation module for the method for learning multivalued mapping according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The embodiments of the present invention will next be described in detail with reference to the drawings.

For a given n(n=1, 2, . . . ) and a given m (m=1, 2, . . . ), a mapping calculation module having an n-dimensional real value input {right arrow over (x)}=(x₁, x₂, . . . , x_(n)) and an m-dimensional real value output {right arrow over (y)}=(y₁, y₂, . . . , y_(m)) is considered.

Determining internal parameters of this mapping calculation module from a given pair of input and output is called learning of mapping. The function of this calculation module is defined by a manifold (surface in high-dimensional space) in (n+m)-dimensional space (x₁, x₂, . . . , x_(n), y₁, y₂, . . . , y_(m)) (hereinafter referred to as “input-output space”) which is formed by combining the input space and the output space. The restoration procedure for derivation of this manifold from a small number of pairs (N pairs) of example data {({right arrow over (x)}_((i)), {right arrow over (y)}_((i)))|i=1, 2, . . . , N} is an approximation of multivalued mapping.

In the above-mentioned references (Japanese Patent Application Laid Open No. 7-93296 and Transactions of the Institute of Electronics, Information and Communication Engineers of Japan A, vol. J78-A, No. 3, pp. 427-439 (1995) “Multivalued Regularization Network”), the inventor of the present invention proposed a method for reducing the learning of a multivalued mapping function to a problem of optimization of energy functional which is defined by a tensor equation directly expressing the multivalued function and a regularization operator constraining smoothness of the function.

However, the algorithm derived in the above-mentioned references cannot perform overall approximation covering all the input-output space, although local approximation can be performed in the vicinity of each piece of example data. Therefore, when only a small amount of example data are available overall approximation covering the entire input-output space is difficult.

Overall approximation using polynomial equations, such as the Lagrange's interpolation method, is a well-known technique for approximation by single-valued functions. However, these methods are said to be poor in local approximation and to cause an oscillation where example data do not exist. Moreover, conventional function approximation methods cannot yield a plurality of possible values for output, since it cannot express a multivalued function.

The present invention realizes an approximation method that simultaneously provides both overall approximation and local approximation.

A case when m=1 is described below. The output variable is denoted y, which can assume a maximum of h (h=1, 2, . . . ) real values. Namely, the mapping is expressed in terms of an h-valued function. Then, the constraining manifold is expressed by the following equation:

y ^(h) F _(h)({right arrow over (x)})+y ^(h−1) F _(h−1)({right arrow over (x)})+ . . . +yF ₁({right arrow over (x)})+F ₀({right arrow over (x)})=0.  (4)

According to the present invention, (h+1) functions of F_(k)(x) (k=0, 1, 2, . . . , h) are expressed in the following equation: $\begin{matrix} {{F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{t}}_{kp}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}.}}}}} & (5) \end{matrix}$

wherein K_(kp)({right arrow over (x)}, {right arrow over (t)}_(kp)) (p=0, 1, 2, . . . , M) is a local base function centering at {right arrow over (t)}_(kp), and φ_(kj)({right arrow over (x)}) is the base of a multi-variable polynomial equation having x_(i) of {right arrow over (x)}=(x₁, . . . , x_(n)) as variables. Thus, F_(k)(x) includes a term that is the linear sum of local base functions and a term of multi-variable polynomial equations (which are defined in an overall view).

In this case, the mapping calculation module has base functions K_(kp)({right arrow over (x)}, {right arrow over (t)}_(kp)), centers of the base functions {right arrow over (t)}_(kp), selection of degree and base of the polynomial equation term, and parameters of factors r_(kp) and s_(kj). Calculation of all of these from example data is not realistic. Especially when the number of example data sets is very small, it is realistic to approximate in a wide view only by the multi-variable polynomial term.

In a case of m>1, a similar discussion can be applied, based on a developed equation of the following equation:

{{right arrow over (y)}−f ₁({right arrow over (x)})}{{right arrow over (y)}−f ₂({right arrow over (x)})} . . . {{right arrow over (y)}−f _(h)){right arrow over (x)})}=0.  (6)

As described later, a case of m=2 can also be shown as an example.

A first embodiment of the present invention will now be described with reference to a flow chart shown in FIG. 1.

A case of the first embodiment is described, wherein m=1, K_(k1)=K_(k2)= . . . =K_(kM) (k=0, 1, . . . , h), M=N, and {right arrow over (t)}kp={right arrow over (x)}(p) (p=1, 2, . . . , N).

(1) A multivalued function expression of a manifold is constructed, wherein an h-valued function is expressed as follows (Step S1):

{y−f ₁({right arrow over (x)})}{y−f ₂({right arrow over (x)})} . . . {y−f _(h)({right arrow over (x)})}=0.  (7)

(2) The above equation is developed, and the following equation is obtained using functions F_(k)(x) (k=0, 1, . . . , h), so that all the coefficients of y^(i) in the terms may be functional terms (Step S2):

y ^(h) F _(h)({right arrow over (x)})+y ^(h−1) F _(h−1)({right arrow over (x)})+ . . . +yF ₁({right arrow over (x)})+F ₀({right arrow over (x)})=0.  (8)

(3) The (h+1) functions F_(k)({right arrow over (x)}) (k=0, 1, . . . , h) are expressed as follows (Step S3): $\begin{matrix} {{{F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{k}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{x}}_{(p)}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}}}}},} & (9) \end{matrix}$

wherein K_(k)({right arrow over (x)}, {right arrow over (x)}_((i))) is a local base function centering at {right arrow over (x)}_((i)), and K_(k)=K_(k1)=K_(k2)= . . . =K_(kM) is assumed. “φ_(kj)({right arrow over (x)})” is the base of the multi-variable polynomial with x_(i) of {right arrow over (x)}=(x₁, . . . , x_(n)) as variables.

(4) An error functional is defined by the following equation (Step 4): $\begin{matrix} {{E\left\lbrack F_{k} \right\rbrack} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\quad \left\{ {{y_{(i)}^{h}{F_{h}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} + {y_{(i)}^{h - 1}\left( {\overset{\rightarrow}{x}}_{(i)} \right)} + \ldots + {y_{(i)}{F_{1}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} + {F_{0}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} \right\}^{2}}}} & (10) \end{matrix}$

(5) A regularization operator S_(k) is defined as a linear combination of quasi-differential operators: $S_{k}{\sum\limits_{i}\quad {{a_{ki}\left( \overset{\rightarrow}{x} \right)}\frac{\partial^{i_{1} + \ldots \quad + i_{n}}}{{\partial x_{1}^{i_{1}}}\ldots \quad {\partial\quad x_{n}^{in}}}}}$

where i=(i₁, i₂, . . . , i_(n)), provided that $\sum\limits_{i}\quad {\equiv {\sum\limits_{i_{1}}\quad {\sum\limits_{i_{2}}{\ldots \sum\limits_{i_{n}}}}}}$

and a_(ki)({right arrow over (x)}) is a function of {right arrow over (x)} (Step 5). Then the regularizing functional of the unknown function F_(k) is

∥S _(k) F _(k)({right arrow over (x)})∥².  (11)

(6) The procedure for minimizing the functional $\begin{matrix} {{E\left\lbrack F_{k} \right\rbrack} = {{E\left\lbrack F_{k} \right\rbrack} + {\sum\limits_{k = 0}^{h}\quad {{S_{k}{F_{k}\left( \overset{\rightarrow}{x} \right)}}}^{2}}}} & (12) \end{matrix}$

is obtained (Step S6) under the condition of $\begin{matrix} {{\sum\limits_{k = 0}^{h}\left( {{\sum\limits_{p = 1}^{M}\quad r_{kp}^{2}} + {\sum\limits_{j = 1}^{d_{k}}\quad s_{kj}^{2}}} \right)} = 1.} & (13) \end{matrix}$

This procedure is obtained through use of a conventional method such as Lagrange's method of indeterminate coefficients for conditioned optimization. Many other standard optimization methods can be applied.

(7) For obtaining f_(j) from F_(k), many methods can be applied for solving the following h-degree algebraic equation:

Λ(F _(k) , y)=y ^(h) F _(h)({right arrow over (x)})+y ^(h−1) F _(h−1)({right arrow over (x)})+ . . . +yF ₁({right arrow over (x)})+F ₀({right arrow over (x)})=0  (14)

(Step S7).

To obtain only one solution for this equation, the Newton-Laphson method can be used, wherein iterative calculation of $\begin{matrix} {y^{\lbrack{k + 1}\rbrack}:={y^{\lbrack k\rbrack} - \frac{\Lambda \left( {F_{k},y^{\lbrack k\rbrack}} \right)}{\Lambda_{y}\left( {F_{k},y^{\lbrack k\rbrack}} \right)}}} & (15) \end{matrix}$

is performed, wherein k denotes a variable representing the number of iterations, and $\begin{matrix} {{\Lambda_{y}\left( {F_{k},y} \right)} = {\frac{\partial\Lambda}{\partial y} = {{{hF}_{h}y^{h - 1}} + \ldots + {{jF}_{j}y^{j - 1}} + \ldots + {F_{1}.}}}} & (16) \end{matrix}$

Other algorithms can be used as that described in Transactions of the Institute of Electronics, Information and Communication Engineers of Japan A, vol. J78-A, No. 3, pp. 427-439 (1995).

A second embodiment will now be described.

A case of the second embodiment will now be described, wherein m=2, h=2, K_(k1)=K_(k2)= . . . =K_(kM)=K_(k) (k=0, 1, . . . , h), M=N, and t_(kp)=x_((p)) (p=1, 2, . . . , N).

(1) A manifold defining a mapping is expressed in a form synthesizing two 2-dimensional vector value functions in n-dimensional space as follows:

{{right arrow over (y)}−f ₁({right arrow over (x)})}{{right arrow over (y)}−f ₂({right arrow over (x)})}={right arrow over (0)},  (17)

wherein

{right arrow over (y)}=f ₁({right arrow over (x)})={f _(1,1)({right arrow over (x)}), f _(1,2)({right arrow over (x)})}^(T),  (18)

and

{right arrow over (y)}=f ₂({right arrow over (x)})={f _(2,1)({right arrow over (x)}), f _(2,2)({right arrow over (x)})}^(T).  (19)

When each component of equation (17) is expressed separately, the following is obtained: $\begin{matrix} {{\left\{ {\overset{\rightarrow}{y} - {f_{1}\left( \overset{\rightarrow}{x} \right)}} \right\} \otimes \left\{ {\overset{\rightarrow}{y} - {f_{2}\left( \overset{\rightarrow}{x} \right)}} \right\}} = {\quad {\left\lbrack {\left. \begin{matrix} {\left\{ {y_{1} - {f_{1,1}\left( \overset{\rightarrow}{x} \right)}} \right\} \quad \left\{ \left( {y_{1} - {f_{2,1}\left( \overset{\rightarrow}{x} \right)}} \right\} \right.} & \left\{ {\left( {y_{1} - {f_{1,1}\left( \overset{\rightarrow}{x} \right)}} \right\} \quad \left\{ {y_{2} - {f_{2,2}\left( \overset{\rightarrow}{x} \right)}} \right\}} \right. \\ {\left\{ {y_{2} - {f_{1,2}\left( \overset{\rightarrow}{x} \right)}} \right\} \quad \left\{ \left( {y_{1} - {f_{2,1}\left( \overset{\rightarrow}{x} \right)}} \right\} \right.} & {\left\{ {y_{2} - {f_{1,2}\left( \overset{\rightarrow}{x} \right)}} \right\} \quad \left\{ {y_{2} - {f_{2,2}\left( \overset{\rightarrow}{x} \right)}} \right\}} \end{matrix} \right\rbrack = {\overset{\rightarrow}{0}.}} \right.}}} & (20) \end{matrix}$

Even if f₁ and f₂ are exchanged with each other, the equation must not change, in consideration of the meaning of the problem. Therefore, the following equation is derived: $\begin{matrix} {{\frac{1}{2}\left\{ {{\left\lbrack {\overset{\rightarrow}{y} - {f_{1}\left( \overset{\rightarrow}{x} \right)}} \right\rbrack \otimes \left\lbrack {\overset{\rightarrow}{y} - {f_{2}\left( \overset{\rightarrow}{x} \right)}} \right\rbrack} + {\left\lbrack {\overset{\rightarrow}{y} - {f_{2}\left( \overset{\rightarrow}{x} \right)}} \right\rbrack \otimes \left\lbrack {\overset{\rightarrow}{y} - {f_{1}\left( \overset{\rightarrow}{x} \right)}} \right\rbrack}} \right\}} = {\overset{\rightarrow}{0}.}} & (21) \end{matrix}$

Then the following three equations are introduced:

y ₁ ² −{f _(1,1)({right arrow over (x)})+f _(2,1)({right arrow over (x)})}y ₁ +f _(1,1)({right arrow over (x)})f _(2,1)({right arrow over (x)})=0,

y ₂ ² −{f _(1,2)({right arrow over (x)})+f _(2,2)({right arrow over (x)})}y ₂ +f _(1,2)({right arrow over (x)})f _(2,2)({right arrow over (x)})=0,

2y ₁ y ₂ −{f _(1,2)({right arrow over (x)})+f _(2,2)({right arrow over (x)})}y ₁ −{f _(1,1)({right arrow over (x)})+f _(2,1)({right arrow over (x)})}y ₂

+{f _(1,1)({right arrow over (x)})f _(2,2)({right arrow over (x)})+f _(1,2)({right arrow over (x)})f _(2,1)({right arrow over (x)})}=0.  (22)

(2) For replacement of the four unknown functions f_(1,1)({right arrow over (x)}), f_(1,2)({right arrow over (x)}), f_(2,1)({right arrow over (x)}) and f_(2,2)({right arrow over (x)}), six unknown functions F_(k)(x) (k=0, 1, 2, 3, 4, 5) are defined as follows:

F ₁({right arrow over (x)})={f _(1,1)({right arrow over (x)})f _(2,1)({right arrow over (x)})}F ₀({right arrow over (x)})

F ₂({right arrow over (x)})={f _(1,2)({right arrow over (x)})f _(2,2)({right arrow over (x)})}F ₀({right arrow over (x)})

F ₃({right arrow over (x)})={−[f _(1,1)({right arrow over (x)})+f _(2,1)({right arrow over (x)})]}F ₀({right arrow over (x)})

F ₄({right arrow over (x)})={−[f _(1,2)({right arrow over (x)})+f _(2,2)({right arrow over (x)})]}F ₀({right arrow over (x)})

F ₅({right arrow over (x)})=[f _(1,1)({right arrow over (x)})f _(2,2)({right arrow over (x)})+f _(1,2)({right arrow over (x)})f _(2,1)({right arrow over (x)})]F ₀({right arrow over (x)}).  (23)

Then all of the three equations (22) become linear with respect to the unknown functions F_(k)(x) (k=0, 1, 2, 3, 4, 5) as follows:

F ₀({right arrow over (x)})y ₁ ² +F ₃({right arrow over (x)})y ₁ +F ₁({right arrow over (x)})=0

F ₀({right arrow over (x)})y ₂ ² +F ₄({right arrow over (x)})y ₂ +F ₂({right arrow over (x)})=0

2F ₀({right arrow over (x)})y ₁ y ₂ +F ₄({right arrow over (x)})y ₁ +F ₃({right arrow over (x)})y ₂ +F ₅({right arrow over (x)})=0  (24)

(3) Each of the unknown functions F_(k)(x) (k=0, 1, 2, 3, 4, 5) is expressed as follows: $\begin{matrix} {{F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{k}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{x}(i)}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}}}}} & (25) \end{matrix}$

wherein K_(k)({right arrow over (x)},{right arrow over (x)}_((i))) is a local base function centering at {right arrow over (x)}_((i)), and φ_(kj)({right arrow over (x)}) is the base of a multi-variable polynomial with {right arrow over (x)} as a variable.

(4) An error function is defined by the following equation: $\begin{matrix} \begin{matrix} {{E\left\lbrack F_{k} \right\rbrack} = \quad {{\sum\limits_{i = 1}^{N}\quad \left\{ {{F_{0}\left( {\overset{\rightarrow}{x}}_{(i)} \right)} + \left( y_{1{(i)}} \right)^{2} + {{F_{3}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}y_{1{(i)}}} + {F_{1}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} \right\}^{2}} +}} \\ {\quad {{\sum\limits_{i = 1}^{N}\quad \left\{ {{{F_{0}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}\left( y_{2{(i)}} \right)^{2}} + {{F_{4}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}y_{2{(i)}}} + {F_{2}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} \right\}^{2}} +}} \\ {\quad {\frac{1}{2}{\sum\limits_{i = 1}^{N}\quad \left\{ {2{F_{0}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}\left( {{y_{1{(i)}}y_{2{(i)}}} + {{F_{4}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}y_{1{(i)}}} +} \right.} \right.}}} \\ \left. \quad {{{F_{3}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}y_{2{(i)}}} + {F_{5}\left( {\overset{\rightarrow}{x}}_{(i)} \right)}} \right\}^{2} \end{matrix} & (26) \end{matrix}$

(5) The regularization operator S_(k) is defined such that $S_{k}{\sum\limits_{i}\quad {{a_{ki}\left( \overset{\rightarrow}{x} \right)}\frac{\partial^{i_{1} + \ldots \quad + i_{n}}}{{\partial x_{1}^{i_{1}}}\ldots \quad {\partial\quad x_{n}^{i_{n}}}}}}$

where i=(i₁, i₂, . . . , i_(n)), provided that $\sum\limits_{i}\quad {\equiv {\sum\limits_{i_{1}}\quad {\sum\limits_{i_{2}}{\ldots \sum\limits_{i_{n}}}}}}$

and a_(ki)({right arrow over (x)}) is a function of {right arrow over (x)}. In that case, the regularizing functional of the unknown function F_(k) is

∥S _(k) F _(k)({right arrow over (x)})∥².   (27)

(6) A procedure is obtained for minimizing the function $\begin{matrix} {{\overset{\_}{E}\left\lbrack F_{k} \right\rbrack} = {{E\left\lbrack F_{k} \right\rbrack} + {\sum\limits_{k = 0}^{5}\quad {{S_{k}{F_{k}\left( \overset{\rightarrow}{x} \right)}}}^{2}}}} & (28) \end{matrix}$

under the condition of ${\sum\limits_{k = 0}^{5}\left( {{\sum\limits_{p = 1}^{M}\quad r_{kp}^{2}} + {\sum\limits_{j = 1}^{d_{k}}\quad s_{kj}^{2}}} \right)} = 1.$

This procedure is performed according to a traditional method such as Lagrange's method of indeterminate coefficients for conditioned optimization. Many other variations of standard optimization methods are possible.

(7) Equation (23) is solved for f_(i,j) in order to obtain f_(i,j) from F_(k). In the present case, equation (23) can be solved easily to yield the following formulas:

The following functions are calculated first: $\begin{matrix} {{f_{+ {,1}}\left( \overset{\rightarrow}{x} \right)} = {\frac{1}{2{F_{0}\left( \overset{\rightarrow}{x} \right)}}\left\lbrack {{- {F_{3}\left( \overset{\rightarrow}{x} \right)}} + \sqrt{\left\{ {F_{3}\left( \overset{\rightarrow}{x} \right)} \right\}^{2} - {4{F_{1}\left( \overset{\rightarrow}{x} \right)}}}} \right\rbrack}} & (29) \\ {{f_{- {,1}}\left( \overset{\rightarrow}{x} \right)} = {\frac{1}{2{F_{0}\left( \overset{\rightarrow}{x} \right)}}\left\lbrack {{- {F_{3}\left( \overset{\rightarrow}{x} \right)}} - \sqrt{\left\{ {F_{3}\left( \overset{\rightarrow}{x} \right)} \right\}^{2} - {4{F_{1}\left( \overset{\rightarrow}{x} \right)}}}} \right\rbrack}} & (30) \\ {{f_{+ {,2}}\left( \overset{\rightarrow}{x} \right)} = {\frac{1}{2{F_{0}\left( \overset{\rightarrow}{x} \right)}}\left\lbrack {{- {F_{4}\left( \overset{\rightarrow}{x} \right)}} + \sqrt{\left\{ {F_{4}\left( \overset{\rightarrow}{x} \right)} \right\}^{2} - {4{F_{2}\left( \overset{\rightarrow}{x} \right)}}}} \right\rbrack}} & (31) \\ {{f_{- {,2}}\left( \overset{\rightarrow}{x} \right)} = {\frac{1}{2{F_{0}\left( \overset{\rightarrow}{x} \right)}}\left\lbrack {{- {F_{4}\left( \overset{\rightarrow}{x} \right)}} - \sqrt{\left\{ {F_{4}\left( \overset{\rightarrow}{x} \right)} \right\}^{2} - {4{F_{2}\left( \overset{\rightarrow}{x} \right)}}}} \right\rbrack}} & (32) \end{matrix}$

Then, out of the two pairs of the solutions,

f ₁({right arrow over (x)})={f _(+,1)({right arrow over (x)}), f _(+,2)({right arrow over (x)})}^(T)

f ₂({right arrow over (x)})={f _(−,1)({right arrow over (x)}), f _(−,2)({right arrow over (x)})}^(T)  (33)

and

f ₁({right arrow over (x)})={f _(+,1)({right arrow over (x)}), f _(−,2)({right arrow over (x)})}^(T)

f ₂({right arrow over (x)})={f _(−,1)({right arrow over (x)}), f _(+,2)({right arrow over (x)})}^(T),  (34)

one pair is selected so that

∥F ₅({right arrow over (x)})−F ₀({right arrow over (x)}){f _(1,1)({right arrow over (x)})f _(2,2)({right arrow over (x)})−f _(1,2)({right arrow over (x)})f _(2,1)({right arrow over (x)})}∥  (35)

may become smaller.

A third embodiment will next be described.

In this embodiment, the calculation procedure of mapping calculation module from the inputs to the outputs is described in reference to the mapping calculation module shown in FIG. 2 and the structure of the module shown in FIG. 3.

In FIG. 3, a unit G_(k) calculates the term of $\begin{matrix} {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{t}}_{kp}} \right)}}},} & (36) \end{matrix}$

and a unit H_(k) calculates the term of $\begin{matrix} {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}.}}} & (37) \end{matrix}$

The unit U is an algorithm for calculating a solution of an h-degree algebraic equation:

Λ(F _(k) , y ^([k]))=F _(h) y ^(h) + . . . +F _(j) y ^(j) + . . . F ₁ y+F ₀=0  (38)

For example, the unit U is an iterative algorithm with an initial value of y=y⁽⁰⁾: $\begin{matrix} {{y^{\lbrack{k + 1}\rbrack}:={y^{\lbrack k\rbrack} - \frac{\Lambda \left( {F_{k},y^{\lbrack k\rbrack}} \right)}{\Lambda_{y}\left( {F_{k},y^{\lbrack k\rbrack}} \right)}}},} & (39) \end{matrix}$

wherein $\begin{matrix} {\Lambda_{y} = {\frac{\partial\Lambda}{\partial y} = {{{hF}_{h}y^{h - 1}} + \ldots + {{jF}_{j}y^{j - 1}} + \ldots + {F_{1}.}}}} & (40) \end{matrix}$

The unit V is the same as Unit U except that it is an algorithm for calculating all the solutions simultaneously. For example, it is an iterative algorithm (Durand-Kerner method): $\begin{matrix} {{{}_{}^{}{}_{}^{\left\lbrack {k + 1} \right\rbrack}}:={{{}_{}^{}{}_{}^{\lbrack k\rbrack}} - \frac{\Lambda \left( {F_{k},{{}_{}^{}{}_{}^{\lbrack k\rbrack}}} \right)}{\prod\limits_{{i = 1},{i \neq j}}^{h}\quad \left( {{{}_{}^{}{}_{}^{\lbrack k\rbrack}} - {{}_{}^{}{}_{}^{\lbrack k\rbrack}}} \right)}}} & (41) \end{matrix}$

wherein j=1, 2, . . . , h).

^(j)y is the j-th root of equation Λ=0 with respect to y, and

^(i)y is the i-th root of equation Λ=0 with respect to y.

Then the mapping calculation module for n inputs and m outputs and function F_(k) (k=0, 1, . . . , h) is calculated for an input {right arrow over (x)} as shown in FIG. 3 according to the following equation: $\begin{matrix} {{F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},{\overset{\rightarrow}{t}}_{kp}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}}}}} & (42) \end{matrix}$

Next, for the case of scalar values, one or all of the h function values are calculated according to the algorithm introduced in the last step of the first embodiment (the part regarding the unit U and the unit V in the description referring to FIG. 3).

For the case of vector values, the algorithm may be used as described in Article (7) of the second embodiment.

The present invention is not limited to the above-described embodiments. Numerous modifications and variations of the present invention are possible in light of the spirit of the present invention, and they are not excluded from the scope of the present invention.

As described above, the learning method for multivalued mapping by the present invention provides the following effects:

According to the present invention, the method comprises the steps of: mathematically expressing a multivalued function directly in Kronecker's tensor product form; developing and replacing the function so as to obtain a linear equation with respect to an unknown function; defining a sum of a linear combination of a local base function and a linear combination of a polynomial base to the replaced unknown function; and learning or structuring a manifold which is defined by the linearized function in the input-output space, from example data, through use of a procedure for optimizing the error and the smoothness constraint. Therefore, mapping learning can be performed from a small amount of data.

Therefore, basic information treatment of non-linear problems may be performed, including applications to artificial neuro-circuit network, visual and auditory pattern information processing by a computer, and kinetic control of a robot arm. 

What is claimed is:
 1. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping for providing a method for approximation of a manifold in (n+m)-dimensional space, by learning a smooth function from n-dimensional input space to m-dimensional output space which optimally approximates m-dimensional vector value data forming a given plurality of layers in n-dimensional space, said method comprising the steps of: (a) mathematically expressing a multivalued function directly in Kronecker's tensor product form; (b) developing and replacing the tensor product form so as to obtain a linear equation with respect to unknown functions; (c) defining the sum of a linear combination of local base functions and a linear combination of polynomial bases with respect to the replaced unknown functions; (d) learning from example data, a manifold which is defined by the linearized function in the input-output space, through use of a procedure for optimizing the error and the smoothness constraint; and (e) applying the manifold learned from the steps (a) to (d) to an artificial neuro-circuit network, to visual or auditory pattern information processing by a computer, or to kinetic control of a robot arm.
 2. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping for providing a method for approximation of a manifold in (n+m)-dimensional space, by obtaining a smooth function from n-dimensional input space to m-dimensional output space which optimally approximates m-dimensional vector value data forming a given plurality of layers in n-dimensional space, said method comprising the steps of: (a) expressing a manifold by synthesizing h m-dimensional vector value functions in n-dimensional space according to the below equations: {{right arrow over (y)}−f ₁({right arrow over (x)})}{{right arrow over (y)}−f ₂({right arrow over (x)})} . . . {{right arrow over (y)}−f _(h)({right arrow over (x)})}=0  (2) wherein “” denotes Kronecker's tensor product, {right arrow over (x)}=(x ₁ , x ₂ , . . . x _(n))^(T), {right arrow over (y)}=(y ₁ , y ₂ , . . . y _(m))^(T), and “^(T)” denotes transposition of vector; (b) developing the above equation and converting it into a linear equation with respect to unknown functions; (c) expressing each unknown function as: ${F_{k}\left( \overset{\rightarrow}{x} \right)} = {{\sum\limits_{p = 1}^{M}\quad {r_{kp}{K_{kp}\left( {\overset{\rightarrow}{x},t_{kp}} \right)}}} + {\sum\limits_{j = 1}^{d_{k}}\quad {s_{kj}{\varphi_{kj}\left( \overset{\rightarrow}{x} \right)}}}}$

wherein K_(kp)(x, t_(kp)), p=(0, 1, 2, . . . M) is a local base function centering at t_(kp), φ_(kj)(x) is the base of a multi-variable polynomial equation having x_(i) of x=(x₁, x₂, . . . x_(n)) as variables, and r_(kp) and s_(kj) are calculated factors; (d) defining an error functional for calculating the error on the left side of Equation (2) for calculating the unknown function F_(k) from example data; (e) defining a regularizing functional as the square of the absolute value or any other norm of the result of the operation in which the operator defining the smoothness constraint of the unknown function is applied to each unknown function, as required; (f) minimizing the error functional and the regularizing function, and deriving a procedure for obtaining the unknown functions F_(k); (g) obtaining a conversion function for calculating f_(j) from the unknown functions F_(k) by formula manipulation method or by numerical approximation algorithm; and (h) applying the conversion function obtained by steps (a) through (g) to an artificial neuro-circuit network, to visual or auditory pattern information processing by a computer, or to kinetic control of a robot arm.
 3. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping according to claim 2, wherein m is
 1. 4. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping according to claim 2, wherein m is
 2. 5. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping according to claim 2, wherein {right arrow over (t)} _(0p) ={right arrow over (t)} _(1p) ={right arrow over (t)} _(2p) = . . . ={right arrow over (t)} _(hp).
 6. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping according to claim 2, wherein N pairs of example data are [({right arrow over (x)}_((i)), {right arrow over (y)}_((i)))|i=1, 2, . . . , N], and M=N, {right arrow over (t)}_(kp)={right arrow over (x)}_((p)) (wherein p=1, 2, . . . , N).
 7. A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping according to claim 2, wherein K_(k1)=K_(k2)= . . . =K_(kM) (wherein k=0, 1, 2, . . . , h). 